
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "177"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 86 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 82 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.116624	-0.324452	0.079946	12.123140	0.945601	54.789765	0.588845	5.010755	0.7452	0.7469	0.2705	14.871549	12.565475
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.669141	-0.342351	0.821541	-0.895787	0.142664	7.886992	0.899564	2.779019	0.7161	0.7154	0.2663	5.305392	5.527544
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.286484	-0.827480	-0.494968	-0.824424	0.463454	1.153990	0.786839	0.357616	0.0321	0.0304	0.0010	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.180658	-0.218845	0.983204	-0.009258	0.181839	22.526302	-0.116009	2.817083	0.6458	0.6650	0.3860	5.661118	5.366647
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.182581	2.828055	0.390937	11.378285	-0.087385	63.094094	0.237531	1.752953	0.6496	0.6580	0.3781	4.779085	4.855354
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.605353	4.132223	-0.188385	17.754669	0.210152	10.832638	0.574163	7.539832	0.6754	0.6666	0.3173	6.450677	6.627527
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.008769	-0.621573	1.126702	2.148414	0.012477	8.401948	1.106584	3.285335	0.7590	0.7539	0.2287	8.992490	8.289495
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.972429	0.259864	2.329956	0.173456	-0.738527	5.952435	-0.680592	0.769740	nan	nan	nan	0.951253	1.287158
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.455568	-0.849186	0.376846	0.860158	-0.144911	35.205111	-0.447669	2.058973	0.6229	0.6457	0.3878	3.838460	4.207396
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.895844	9.979623	0.279722	5.544300	1.216945	4.898774	3.209621	2.468858	0.0360	0.0338	0.0018	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.449020	1.571026	-0.188199	13.893373	0.272293	35.991519	1.765426	3.847612	0.5912	0.6010	0.3757	25.436205	29.032941
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.471717	0.911440	-0.685991	12.174649	-0.998805	0.755472	0.079681	1.053205	0.6482	0.6499	0.3942	4.093158	4.423244
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.024902	0.398263	-0.486309	11.325975	-0.481385	1.186507	-0.228505	0.868379	0.6492	0.6496	0.3989	3.975059	4.295179
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.679029	1.076084	-0.912891	13.217402	-0.261186	6.273305	0.728126	3.162618	0.6524	0.6519	0.3974	3.674109	3.536292
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.228309	0.545293	-0.490211	14.034508	-0.124720	1.776949	0.456705	2.128508	0.6517	0.6498	0.3921	4.425794	4.745472
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.401600	0.454120	-0.473991	16.471397	-0.458147	5.323666	0.786889	2.906097	0.6364	0.6369	0.4035	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.459741	0.266519	-0.877565	0.976403	0.181954	0.180782	-0.270059	0.996086	0.0342	0.0308	0.0015	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.243478	0.058157	-0.835983	8.294682	-0.657334	89.852496	1.111509	2.357470	0.6512	0.6576	0.3934	3.519983	3.700502
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.680714	-0.129438	-0.565779	-0.721425	-0.165356	16.050820	2.006542	5.221070	0.6742	0.6906	0.3995	6.225825	6.734964
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.127166	-0.001790	-0.840146	1.970125	-0.337360	58.873134	-0.631277	3.260266	0.7226	0.7303	0.3497	5.750517	6.645738
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.166076	-0.551093	-0.872870	-0.080740	-0.193193	32.912913	1.779459	3.871938	0.6700	0.6868	0.3953	4.259741	4.424930
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.695543	0.340978	-0.590007	14.003759	-0.991393	1.518012	0.216274	1.977290	0.6630	0.6693	0.3966	4.500256	4.641539
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.841174	-0.347318	-0.750334	2.070382	-0.907266	78.663616	-0.145855	3.520613	0.6753	0.6827	0.3974	5.147579	5.313854
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.284033	-0.288858	-0.863304	-0.708892	-0.447196	47.924674	-0.261135	3.021592	0.0344	0.0301	0.0013	nan	nan
2459570	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.666073	5.457112	7.990968	7.184930	5.543044	6.462730	3.717658	4.847548	0.6661	0.6937	0.3791	7.454502	8.694507
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.310568	-0.501510	-0.834488	0.250832	-0.497068	25.236668	-0.449865	3.589747	0.7294	0.7381	0.2671	4.367601	4.585785
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	1.62%	-0.305520	0.142026	-0.802811	3.164173	-0.797835	1.777456	1.007528	3.982367	0.6572	0.6716	0.4140	1.506100	1.657594
2459565	digital_ok	0.00%	-	-	-	86.67%	0.00%	-0.056602	-0.056602	-0.073735	-0.073735	0.553413	0.553413	0.061081	0.061081	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.361185	-1.399784	-4.212612	-3.407544	8.765497	7.526813	0.355694	3.078666	0.6607	0.6708	0.4060	2.953229	3.031829
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.307978	3.295050	24.055331	16.231544	13.239400	11.501246	-1.447254	1.212332	0.6542	0.6775	0.4054	3.194611	3.290441
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.191757	0.489944	-0.459170	2.877562	-0.152213	-0.268271	0.100719	3.862050	0.6731	0.6852	0.3969	1.577497	1.615900
2459560	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.766948	0.531342	4.992328	9.521882	0.507986	-0.792832	0.641704	2.920406	0.6566	0.6444	0.4000	3.381697	3.260554
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.225820	-1.266785	0.157046	-1.683742	-0.674049	-0.018706	-1.093993	0.561241	0.6535	0.6732	0.4008	1.554561	1.580698
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.064232	0.748530	-0.365226	9.578664	-0.415483	30.635701	1.304084	6.884758	0.6598	0.6624	0.4121	3.742063	3.750135
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0324	0.0286	0.0015	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.119596	0.293173	-0.503403	3.524786	-0.234352	63.556917	0.893754	7.240486	0.6649	0.6727	0.4027	3.983128	4.108829
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6738	0.6870	0.4138	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6677	0.6812	0.4110	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.854106	0.272053	-0.572622	-0.494362	-0.482949	2.537068	0.012046	0.455278	0.6793	0.6930	0.4048	1.251389	1.385537
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.078663	-0.578992	-0.424503	-0.357395	-0.409919	1.020772	0.309626	0.548560	0.6688	0.6849	0.4057	1.503099	1.528833
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0432	0.0414	0.0007	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.595042	-0.350197	-0.916430	-0.880244	-0.377422	-0.143563	-0.291415	0.537330	0.7201	0.7204	0.3825	1.413977	1.445287
2459541	digital_ok	0.00%	1.34%	1.79%	0.00%	21.88%	0.00%	0.938749	-0.088398	-0.585860	-1.040199	-0.640172	-0.503038	-0.043900	1.030205	0.7118	0.7073	0.3582	1.433431	1.517074
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.89%	-0.126570	-0.266549	-0.680697	-0.076068	-0.096306	-0.476599	0.016168	0.781680	0.7255	0.7203	0.3678	1.482341	1.524246
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.320868	-0.196883	-0.854173	-0.817015	-0.066717	1.347019	0.440146	0.300266	0.6924	0.7016	0.4478	0.000000	0.000000
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.504061	-0.448708	-0.495679	-0.010307	0.437114	-0.318825	0.506960	0.675422	0.7651	0.7459	0.3853	1.635556	1.581211
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.152341	-0.605191	-0.456365	0.008091	1.238821	0.196797	0.796521	1.334472	0.7552	0.7365	0.3773	1.479067	1.527553
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.055666	-0.021611	-0.363711	-0.577878	-0.636294	1.351072	1.312034	1.902883	0.6913	0.7003	0.4181	1.554764	1.538857
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	0.717629	-0.214537	-0.260506	-0.328917	-0.767653	1.594842	1.290820	1.103869	0.6932	0.7015	0.4175	1.550384	1.585995
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.130469	-0.101808	-0.852718	-0.816578	0.121895	5.351971	-0.554785	0.204500	0.0344	0.0330	0.0012	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.812758	-0.685418	-0.410075	-0.653567	-0.796438	0.102766	0.302817	1.234027	0.8921	0.9200	0.2288	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.307425	-0.439800	-0.492740	-0.721077	-0.629913	5.308388	-0.560670	0.745750	0.0814	0.0660	0.0040	0.906055	0.902864
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.302393	-0.244018	-0.177587	-0.899011	-0.810924	0.118952	0.182476	1.431908	0.8614	0.8485	0.4671	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.004276	0.110929	0.446893	-0.857779	-0.882933	0.388648	-0.357410	-0.053838	0.1069	0.0946	0.0082	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.179596	0.055862	0.274444	-0.962669	-0.511408	-0.494071	-0.616966	1.528433	0.1061	0.0969	0.0083	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.134350	-0.069267	-0.012482	-0.915716	-0.788146	-0.344306	2.989313	1.858179	0.1054	0.0978	0.0051	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.103461	0.016651	0.077310	-0.885344	-0.598330	-0.362475	-0.765591	0.281321	0.1028	0.0918	0.0091	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.263437	0.079608	0.260774	-1.008888	-0.446928	-0.337464	-0.573500	0.272567	0.1003	0.0939	0.0076	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.340540	0.068240	-0.075240	-0.831941	-1.209920	-0.592387	-0.508749	-0.152332	0.1248	0.1005	0.0103	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.251046	0.188363	-0.125090	-0.814988	-1.119354	-0.688970	-0.420267	0.507198	0.1106	0.1055	0.0096	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.118861	0.139510	0.024883	-0.560509	-1.237909	-0.163103	-1.011147	0.442594	0.1061	0.0888	0.0088	0.000000	0.000000
2459492	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.215214	0.184631	-0.426051	-0.597431	-0.267898	1.553439	2.364186	3.030206	0.0835	0.0959	0.0092	0.000000	0.000000
2459491	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.108307	0.003364	-0.555455	-0.470760	-0.190497	2.985910	0.718673	0.632593	0.0925	0.1089	0.0071	0.000000	0.000000
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.231171	0.066038	-0.999797	-0.074423	-0.076890	0.663939	0.674099	0.282996	0.7301	0.7913	0.3767	0.000000	0.000000
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.619333	-0.021277	-0.704860	-0.667471	-0.188269	2.320529	-0.041069	0.743902	0.7528	0.8005	0.3243	0.000000	0.000000
2459488	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.757201	0.167144	-0.622122	-0.264826	0.754000	4.964668	4.568602	4.680808	0.7513	0.8021	0.3589	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 177 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 177, Node 12:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶